Histograms are used to characterize data sets, for example in Transfer Function design, because of their relatively high information content in a relatively simple format. Some data sets have uncalibrated and/or non-spatially defined features and can include data associated with features of interest as well as minor features, noise and/or materials with overlapping value ranges.
Direct Volume Rendering (“DVR”) has been used in medical visualization research for a number of years. DVR can be generally described as rendering visual images directly from volume data without relying on graphic constructs of boundaries and surfaces, thereby providing a more complete visualization of internal structures from 3-D data. DVR holds promise for its diagnostic potential in analyzing medical image volumes. Slice-by-slice viewing of medical data may be increasingly difficult for the large data sets now provided by imaging modalities raising issues of information and data overload and clinical feasibility with current radiology staffing levels. See, e.g., Addressing the Coming Radiology Crisis: The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process (TRIP™) Initiative, Andriole et al., at URL scarnet.net/trip/pdf/TRIP_White_Paper.pdf (November 2003). In some modalities, patient data sets can have large volumes, such as greater than 1 gigabyte, and can even commonly exceed tens or hundreds of gigabytes.
Despite its potential, DVR has not achieved widespread use for non-research medical imaging, particularly in computer network systems with visualization pipelines. This may be because DVR may need time-consuming manual adjustment using conventional transfer functions (TF) and/or editing tools. That is, the TF construction can be relatively complex and/or the tissue separation abilities may not be sufficient, particularly where dissimilar tissues have similar intensity values limiting the ability to generate diagnostic clinical renderings.